Over the past several decades, radar and similar imaging technologies have greatly improved. For example, the advent of three-dimensional laser detection and ranging (ladar) systems has greatly increased the ability to detect objects of interest by generating imaging data with much greater resolution than was possible with predecessor technologies. A ladar device is capable of digitizing as much as a gigapoint—one billion points—for a single scene. Such high resolution potentially vastly improves the possibility of target detection in the imaged scene.
Two limitations potentially hamper the ability to detect targets using such a ladar system. First, in the case of ladar and other line-of-sight data gathering systems, targets can be concealed by intervening obstructions. For example, if a ground-based target is partially sheltered by foliage or another obstruction between a ladar system and the target, detection of the target becomes more difficult. To take a more specific example, if a vehicle is parked under a tree, data generated by an aerial ladar system may not clearly indicate the presence of the vehicle. Although the tree is at least a partially permeable obstruction, the presence of the tree changes the profile of the data collected and thus obscures the presence of the ground-based target.
Second, the processing capability required to process enormous, gigapoint ladar images is overwhelming. Computer processing hardware performance has vastly improved, but not enough to completely process such a wealth of data. Computing time for processes such as mesh generation or sorting points may scale too slowly to be practical using available computing resources. For a number of raw data points, N, processing times for mesh generation or sorting points become practically unworkable for very large numbers of data points. Conventional methods involve processing times on the order of N log (N). To successfully meet objects of ladar and other sophisticated detection systems, more rapid detection of targets is desired than is possible with such a conventional processing system.
To make processing ladar data practical, a number of steps to scale the vast number of raw data points must be minimized, parallelized, or simply eliminated. One method to reduce the volume of raw data is to sample the available data by selecting a subset of the available data points. Typically, sampling involves selecting a representative point from each of a number of zones from a pre-selected grid. Unfortunately, reducing the number of data points in such a manner reduces available spatial precision in resolving the area being scanned.
One way to try to balance desires for high precision and tractable processing times is to allow a ladar operator to select and re-select alternative regions of interest in an area of study and to adjust spatial sampling resolution for those regions of interest. In this manner, the user can have desired precision and resolution on an as-desired basis, thereby allowing the user the greatest possible precision where the user wants it while not overwhelming the capacity of the ladar processing system.
However, even if a ladar operator chooses to highlight a region of interest including a partially-obscured target, the processed data may not reveal the presence of the target to the operator. Thus, there is an unmet need in the art to improve detection of targets, particularly where the targets may be at least partially obscured from a line-of-sight view by intervening objects.